Neo4j Vector Index Skill

When to Use

• Creating a vector index (CREATE VECTOR INDEX) on nodes or relationships
• Running vector similarity / nearest-neighbor search
• Storing embeddings on graph nodes during ingestion
• Indexing/querying embeddings already written by GDS algorithms
• Choosing similarity function, dimensions, HNSW params, or quantization
• Using SEARCH clause (2026.01+) or db.index.vector.queryNodes() (2025.x)
• Batch-updating embeddings after model change
• Combining vector results with immediate graph neighborhood (full retrieval_query pipelines → neo4j-graphrag-skill)
• Hybrid search that combines vector results with fulltext or other ranked sources

When NOT to Use

• GraphRAG pipelines (VectorCypherRetriever, HybridCypherRetriever, retrieval_query) → neo4j-graphrag-skill
• Fulltext-only / keyword-only search (FULLTEXT INDEX, db.index.fulltext.queryNodes) → neo4j-cypher-skill
• Computing GDS graph embeddings (FastRP, Node2Vec, GraphSAGE) → neo4j-gds-skill
• Index admin (list all indexes, drop range/text/lookup indexes) → neo4j-cypher-skill

Pre-flight — Determine Version

Drives syntax choice:

CALL dbms.components() YIELD versions RETURN versions[0] AS neo4j_version

Step 1 — Create Vector Index

Node index (single label):

CYPHER 25
CREATE VECTOR INDEX chunk_embedding IF NOT EXISTS
FOR (c:Chunk) ON (c.embedding)
OPTIONS {
  indexConfig: {
    `vector.dimensions`: 1536,
    `vector.similarity_function`: 'cosine',
    `vector.quantization.type`: 'SCALAR',
    `vector.hnsw.m`: 16,
    `vector.hnsw.ef_construction`: 100
  }
}

Node index with filterable properties [2026.01+] — WITH declares which properties can be used in SEARCH ... WHERE:

CYPHER 25
CREATE VECTOR INDEX chunk_embedding IF NOT EXISTS
FOR (c:Chunk) ON (c.embedding)
WITH [c.source, c.lang, c.published_year]  // stored as metadata; filterable in SEARCH WHERE
OPTIONS { indexConfig: { `vector.dimensions`: 1536, `vector.similarity_function`: 'cosine' } }

Multi-label index with filterable properties [2026.01+]:

CYPHER 25
CREATE VECTOR INDEX doc_embedding IF NOT EXISTS
FOR (n:Document|Article) ON n.embedding
WITH [n.author, n.published_year, n.lang]
OPTIONS { indexConfig: { `vector.dimensions`: 1536, `vector.similarity_function`: 'cosine' } }

Relationship index:

CYPHER 25
CREATE VECTOR INDEX rel_embedding IF NOT EXISTS
FOR ()-[r:HAS_CHUNK]-() ON (r.embedding)
OPTIONS { indexConfig: { `vector.dimensions`: 768, `vector.similarity_function`: 'cosine' } }

WITH property types — only scalar types allowed: INTEGER, FLOAT, STRING, BOOLEAN, DATE, ZONED DATETIME, LOCAL DATETIME, ZONED TIME, LOCAL TIME, DURATION. Not allowed: LIST, POINT, or the vector property itself.

Index config reference:

Unquantized vectors: set vector.quantization.type: 'none' alone — on 2026.06, vector.quantization.enabled: false without it errors (fixed 2026.07).

Similarity function choice:

Index providers — latest selected automatically; not specifiable in Cypher 25. Check with SHOW VECTOR INDEXES YIELD name, indexProvider:

Changing quantization type, expansion factor, or provider requires drop + re-create + re-population. Indexes built before 2026.08 and rarely updated: re-create for 2026.09 rescored-binary/scalar speedup (~5× latency and throughput).

Memory — vector index files live in OS filesystem cache, not page cache. Leave RAM for: HNSW graph ≈ 8 B × vectors × vector.hnsw.m plus vector values (full precision ≈ 4 B × dims × vectors; scalar ÷4; binary ÷32). Size page cache for vector properties only if queries return or re-rank them. Details → Vector index memory configuration.

Step 2 — Wait for Index ONLINE

Index builds asynchronously — do NOT query until ONLINE:

SHOW VECTOR INDEXES YIELD name, state, populationPercent
WHERE name = 'chunk_embedding'
RETURN name, state, populationPercent

Poll every 5s until state = 'ONLINE' and populationPercent = 100.0. If state = 'FAILED' → stop, check logs.

Shell poll (cypher-shell):

until cypher-shell -u neo4j -p "$NEO4J_PASSWORD" \
  "SHOW VECTOR INDEXES YIELD name, state WHERE name='chunk_embedding' RETURN state" \
  | grep -q ONLINE; do
  sleep 5
done

Step 3 — Ingest Embeddings

Batch UNWIND pattern (use for > 100 nodes — never one-node-per-transaction):

from neo4j import GraphDatabase

driver = GraphDatabase.driver(uri, auth=(user, password))

def embed_batch(texts: list[str]) -> list[list[float]]:
    response = openai_client.embeddings.create(
        model="text-embedding-3-small", input=texts
    )
    return [r.embedding for r in response.data]

def store_embeddings(records: list[dict], batch_size: int = 500):
    expected_dim = 1536  # must match vector.dimensions
    texts = [r["text"] for r in records]
    embeddings = embed_batch(texts)
    for emb in embeddings:
        assert len(emb) == expected_dim, f"Dim mismatch: {len(emb)} != {expected_dim}"
    rows = [{"id": r["id"], "embedding": emb}
            for r, emb in zip(records, embeddings)]
    for i in range(0, len(rows), batch_size):
        driver.execute_query(
            "UNWIND $rows AS row MATCH (c:Chunk {id: row.id}) SET c.embedding = row.embedding",
            rows=rows[i:i+batch_size]
        )

❌ Never create index after embeddings are already stored — always create index first. ✅ Create index → poll ONLINE → ingest embeddings.

Step 4 — Run Vector Search

SEARCH clause (2026.01+, preferred)

CYPHER 25
MATCH (c:Chunk)
  SEARCH c IN (
    VECTOR INDEX chunk_embedding
    FOR $queryEmbedding
    LIMIT 10
  ) SCORE AS score
RETURN c.text, score
ORDER BY score DESC

With in-index filter [2026.01+] — properties must be declared in WITH at index creation:

// Index must have been created with: WITH [c.source, c.lang, c.published_year]
CYPHER 25
MATCH (c:Chunk)
  SEARCH c IN (
    VECTOR INDEX chunk_embedding
    FOR $queryEmbedding
    WHERE c.source = $source AND c.lang = 'en' AND c.published_year >= 2024
    LIMIT 10
  ) SCORE AS score
RETURN c.text, c.source, score
ORDER BY score DESC

Filtering strategy — choose one:

In-index WHERE hard limits [2026.01+]:

• Property must be listed in WITH [...] at index creation — undeclared properties silently fall back to post-filtering
• AND predicates only — no OR, NOT, string ops. IN list membership allowed [2026.06+]
• Scalar types only: INTEGER, FLOAT, STRING, BOOLEAN, temporal types — not VECTOR/LIST/POINT

Post-filter pattern (2025.x or arbitrary predicates)

CYPHER 25
CALL db.index.vector.queryNodes('chunk_embedding', 50, $queryEmbedding)
YIELD node AS c, score
WHERE c.source = $source    // post-filter: fetch more, then filter
RETURN c.text, score
ORDER BY score DESC LIMIT 10

Relationship index procedure:

CYPHER 25
CALL db.index.vector.queryRelationships('rel_embedding', 5, $queryEmbedding)
YIELD relationship AS r, score
RETURN r.text, score

SEARCH clause hard limits (all versions):

• Index name cannot be a parameter ($indexName not allowed — use literal string)
• Binding variable must come from the enclosing MATCH pattern
• Query vector cannot reference the binding variable

Step 5 — Combine with Graph Traversal (simple cases)

Vector search as entry point, then graph hop:

CYPHER 25
MATCH (c:Chunk)
  SEARCH c IN (
    VECTOR INDEX chunk_embedding
    FOR $queryEmbedding
    LIMIT 10
  ) SCORE AS score
MATCH (c)<-[:HAS_CHUNK]-(a:Article)
OPTIONAL MATCH (a)-[:MENTIONS]->(org:Organization)
RETURN c.text, a.title, score, collect(DISTINCT org.name) AS organizations
ORDER BY score DESC

For full retrieval_query pipelines, HybridCypherRetriever, or neo4j-graphrag library → delegate to neo4j-graphrag-skill.

Step 6 — Hybrid Search

Use hybrid search when one signal misses useful candidates: semantic vectors miss exact terms, lexical fulltext misses paraphrases, structural graph signals find topology not present in text. The common pattern is vector + fulltext, but the same approach works for several vector indexes, GDS-written embeddings, graph traversal scores, or any two+ ranked/scored sources. Load references/hybrid-search.md and apply its query shape.

Rules:

• Run each source independently; rank each by score DESC, stable_id ASC.
• Combine by rank, not raw scores; fulltext and vector scores are not comparable.
• Every UNION ALL branch returns same columns: matched node + contribution.
• Use sourceK > finalK; combine before final limiting.
• Sum contributions per node; order final rows by wrrf DESC, stable_id ASC.
• Add more sources with extra UNION ALL branches and new sourceWeights keys.

Embedding Provider Quick-Reference

vector.dimensions must exactly match model output — no auto-truncation.